本文整理汇总了Python中networkx.compose函数的典型用法代码示例。如果您正苦于以下问题：Python compose函数的具体用法？Python compose怎么用？Python compose使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了compose函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setUp
def setUp(self):
self.T1 = self.graph()
self.T2 = self.graph()
self.T2.add_node(1)
self.T3 = self.graph()
self.T3.add_nodes_from(range(5))
edges = [(i,i+1) for i in range(4)]
self.T3.add_edges_from(edges)
self.T5 = self.multigraph()
self.T5.add_nodes_from(range(5))
edges = [(i,i+1) for i in range(4)]
self.T5.add_edges_from(edges)
self.T6 = self.graph()
self.T6.add_nodes_from([6,7])
self.T6.add_edge(6,7)
self.F1 = nx.compose(self.T6, self.T3)
self.N4 = self.graph()
self.N4.add_node(1)
self.N4.add_edge(1,1)
self.N5 = self.graph()
self.N5.add_nodes_from(range(5))
self.N6 = self.graph()
self.N6.add_nodes_from(range(3))
self.N6.add_edges_from([(0,1),(1,2),(2,0)])
self.NF1 = nx.compose(self.T6,self.N6)示例2: load_semantic_network
def load_semantic_network():
# load several files into a single nx graph
S = load_file_to_graph("../ontologies/stuff_ontology.txt")
A = load_file_to_graph("../ontologies/attribute_ontology.txt")
C = load_file_to_graph("../ontologies/context_knowledge.txt")
X = load_file_to_graph("../ontologies/activity_ontology.txt")
G = nx.compose(S,A)
G = nx.compose(G,C)
G = nx.compose(G,X)
return G示例3: load_semantic_network
def load_semantic_network():
# load several files into a single nx graph
filePath = os.path.dirname(os.path.abspath(__file__))
S = load_file_to_graph(filePath +"/ontologies/stuff_ontology.txt")
A = load_file_to_graph(filePath + "/ontologies/attribute_ontology.txt")
C = load_file_to_graph(filePath + "/ontologies/context_knowledge.txt")
X = load_file_to_graph(filePath + "/ontologies/activity_ontology.txt")
G = nx.compose(S,A)
G = nx.compose(G,C)
G = nx.compose(G,X)
return G示例4: outputGraph
def outputGraph(self, filename, _filter=None):
if filename not in self._graphTime:
self._graphTime[filename] = 0
if (time.time() - self._graphTime[filename]) > 10:
# print 'pre-Update Graph: %s' % filename
G = nx.DiGraph()
plt.clf()
for edge in self._edges:
if _filter is None:
G.add_edge(edge[0], edge[1])
elif _filter(edge[0]) or _filter(edge[1]):
G.add_edge(edge[0], edge[1])
try:
G1 = dfs_tree(G, u"0015")
G2 = dfs_tree(G, u"0013")
G3 = nx.compose(G1, G2)
G4 = dfs_tree(G, u"0017")
G = nx.compose(G3, G4)
except:
pass
relabel = {}
newToOld = {}
for n in G.nodes():
if n in self._berths and self._berths[n] is not None:
relabel[n] = self._berths[n]
newToOld[self._berths[n]] = n
nx.relabel_nodes(G, relabel, False)
colours = []
for n in G.nodes():
if n in newToOld:
n = newToOld[n]
if n in self._berths and self._berths[n] is not None:
colours.append("r")
else:
colours.append("g")
pos = nx.graphviz_layout(G, prog="dot")
nx.draw_networkx_nodes(G, pos, node_color=colours, node_shape="s", node_size=900)
nx.draw_networkx_edges(G, pos)
nx.draw_networkx_labels(G, pos)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(16.0, 25.0)
plt.axis("off")
self.outputFile(plt, filename)
self._graphTime[filename] = time.time()示例5: snowball_round
def snowball_round(G,seeds,myspace=False):
"""Function takes a base graph, and a list of seeds
and builds out the network data by accessing the
Google SocialGraph API."""
t0=time()
if myspace:
seeds=get_myspace_url(seeds)
sb_data=[]
for s in range(0,len(seeds)):
s_sg=get_sg(seeds[s])
new_ego,pen=create_egonet(s_sg) # Create ego net of seed
# Compose new network data into old abse graph
for p in pen:
sb_data.append(p)
if s<1:
sb_net=nx.compose(G,new_ego)
else:
sb_net=nx.compose(new_ego,sb_net)
del new_ego
if s==round(len(seeds)*0.2):
# Simple progress output, useful for long jobs
sb_net.name='20% complete'
nx.info(sb_net)
print 'AT: '+strftime('%m/%d/%Y, %H:%M:%S', gmtime())
print ''
if s==round(len(seeds)*0.4):
sb_net.name='40% complete'
nx.info(sb_net)
print 'AT: '+strftime('%m/%d/%Y, %H:%M:%S', gmtime())
print ''
if s==round(len(seeds)*0.6):
sb_net.name='60% complete'
nx.info(sb_net)
print 'AT: '+strftime('%m/%d/%Y, %H:%M:%S', gmtime())
print ''
if s==round(len(seeds)*0.8):
sb_net.name='80% complete'
nx.info(sb_net)
print 'AT: '+strftime('%m/%d/%Y, %H:%M:%S', gmtime())
print ''
if s==len(seeds)-1:
print 'NEW NETWORK COMPLETE!'
print 'AT: '+strftime('%m/%d/%Y, %H:%M:%S', gmtime())
sb_net.name=G.name+'--> '
# Return newly discovered seeds
sb_data=array(sb_data)
sb_data.flatten()
sb_data=unique(sb_data)
nx.info(sb_net)
return sb_net,sb_data示例6: main
def main():
'''
This is the main function
http://networkx.lanl.gov/reference/algorithms.operators.html
'''
# Get distance matrices
walking_times = read_weights_from_file(walking_time_filename)
shuttle_times = read_weights_from_file(shuttle_time_filename)
shuttle_connection_times = read_weights_from_file(shuttle_connection_time_filename)
outdoorness_matrix = read_weights_from_file(outdoorness_filename)
#print outdoorness_matrix
# Add penalties
shuttle_connection_times = apply_penalty(shuttle_connection_times, shuttle_penalty/2, 'add') # /2 because we get in and out the shuttle, so we don't want to have a double penalty
walking_times = apply_penalty(walking_times, walking_penalty , 'multiply')
walking_times = apply_outdoor_penalty(walking_times, outdoorness_matrix, outdoorness_penalty)
# Create subgraphs
walking_graph = nx.DiGraph(data=walking_times)
#print G.edges(data=True)
walking_graph = nx.relabel_nodes(walking_graph,convert_list_to_dict(read_node_labels(walking_time_filename)))
print 'walking_graph', walking_graph.edges(data=True)
shuttle_graph = nx.DiGraph(data=shuttle_times)
shuttle_graph = nx.relabel_nodes(shuttle_graph,convert_list_to_dict(read_node_labels(shuttle_time_filename)))
print 'shuttle_graph', shuttle_graph.edges(data=True)
shuttle_connection_graph = nx.DiGraph(data=shuttle_connection_times)
shuttle_connection_graph = nx.relabel_nodes(shuttle_connection_graph,convert_list_to_dict(read_node_labels(shuttle_connection_time_filename)))
print 'shuttle_connection_graph', shuttle_connection_graph.edges(data=True)
# Create main graph
main_graph = nx.compose(walking_graph, shuttle_graph)
print 'main_graph', main_graph.edges(data=True)
main_graph = nx.compose(main_graph, shuttle_connection_graph)
print 'main_graph', main_graph.edges(data=True)
# Compute the shortest paths and path lengths between nodes in the graph.
# http://networkx.lanl.gov/reference/algorithms.shortest_paths.html
compute_shortest_path(main_graph, '32', 'NW86')
compute_shortest_path(main_graph, 'W7', 'W20')
compute_shortest_path(main_graph, '50', '35')
#print nx.dijkstra_predecessor_and_distance(main_graph, 'NW86')
# Compute shortest paths and lengths in a weighted graph G. TODO: Return farthest region.
print nx.single_source_dijkstra(main_graph, '32', 'NW86')
# Compute KSP (k-shortest paths) using https://github.com/Pent00/YenKSP
yenksp_digraph = convert_nx_digraph_into_yenksp_digraph(main_graph)
print ksp_yen(yenksp_digraph, 'NW86', '32', 2)示例7: test_compose_multigraph
def test_compose_multigraph():
G = nx.MultiGraph()
G.add_edge(1, 2, key=0)
G.add_edge(1, 2, key=1)
H = nx.MultiGraph()
H.add_edge(3, 4, key=0)
H.add_edge(3, 4, key=1)
GH = nx.compose(G, H)
assert_equal(set(GH), set(G) | set(H))
assert_equal(set(GH.edges(keys=True)), set(G.edges(keys=True)) | set(H.edges(keys=True)))
H.add_edge(1, 2, key=2)
GH = nx.compose(G, H)
assert_equal(set(GH), set(G) | set(H))
assert_equal(set(GH.edges(keys=True)), set(G.edges(keys=True)) | set(H.edges(keys=True)))示例8: graph
def graph(self):
graph = nx.DiGraph()
# Link all antecedents to me by decomposing
# FuzzyVariableTermAggregate down to just FuzzyVariableTerms
for t in self.antecedent_terms:
assert isinstance(t, FuzzyVariableTerm)
graph.add_path([t, self])
graph = nx.compose(graph, t.parent_variable.graph)
# Link all consequents from me
for c in self.consequent:
assert isinstance(c, WeightedConsequent)
graph.add_path([self, c.term])
graph = nx.compose(graph, c.term.parent_variable.graph)
return graph示例9: search
def search(self,concept):
global G, mentioned_concepts
new_subgraph = nx.DiGraph()
# ('ConceptuallyRelatedTo',1), ('AtLocation',2), ('DerivedFrom', 1)
for relation, lim in [('IsA',3), ('PartOf', 2),('HasContext', 2)]:
for node in mentioned_concepts:
json_document = json.dumps(cnet.search(rel=relation, start=node, end=concept, limit=lim))
decoder = json.JSONDecoder()
json_obj = decoder.decode(json_document)
new_subgraph = nx.compose(new_subgraph,self.parse_json_to_graph(json_obj, start=node, end=concept))
json_document = json.dumps(cnet.search(rel=relation,start=concept, end=node, limit=lim))
decoder = json.JSONDecoder()
json_obj = decoder.decode(json_document)
new_subgraph = nx.compose(new_subgraph, self.parse_json_to_graph(json_obj, start=concept, end=node))
G = nx.compose(G,new_subgraph) 示例10: graph_from_set
def graph_from_set(df):
"""
The co-ocurrence graph for a set of tweets is the composition of the individual
complete k-graphs, for each tweet. In other words, each tweet in the set forms
a k-clique of the composed graph, and cliques are connected when distinct tweets
have at least one hashtag in common.
Each already existing node/hashtag that is seen in a new tweet will take on the
timestamp of the new tweet containing it.
Parameters
----------
df : Pandas DataFrame
to extract tweet info from, usually within a time window.
Return
------
G : NetworkX Graph
composition graph on all tweets' hashtags in df
"""
G = nx.Graph(time=df.time.max()) # initialize empty graph with latest timestamp
for i in df.itertuples():
tw_no, tags, time = i
if len(tags) < 2: # skip tweets with no hashtag co-occurrence
continue
H = graph_from_tweet(df, tw_no) # current tweet's complete k-graph
G = nx.compose(G, H) # add new edges and nodes found in H not already in G
return G示例11: combine_graph
def combine_graph(Gs, opts):
# combine the graphs in the list Gs
G = Gs[0]
for i in range(1,len(Gs)):
G = nx.compose(G, Gs[i])
# position of each nodes
pos = nx.spring_layout(G) # Fruchterman-Reingold force-directed algorithm.
# plot nodes and edges separately
for i in range(len(Gs)):
nodes = Gs[i].nodes()
edges = Gs[i].edges()
opt = opts[i]
nx.draw_networkx_nodes(G, pos, nodelist = nodes,
node_color = opt['node_color'],
alpha = float(opt['node_alpha']),
node_size = int(opt['node_size']))
nx.draw_networkx_edges(G, pos, edgelist = edges,
edge_color = opt['edge_color'],
alpha = float(opt['edge_alpha']),
width = int(opt['edge_width']))
# label the nodes
nx.draw_networkx_labels(G, pos)
plt.show()示例12: update_graph
def update_graph(self, list_solutions):
''' update__graph
Parameters:
list_solutions: A list of paths returned by different ants
A path is a list of NODES (not node indexes)
Given a list of solutions (list of paths), updates the global graph.
Return:
Returns a list of graphs of the solutions
'''
list_graphs_return = list()
for solution in list_solutions:
# [[(1080243414620259090, {'node': <aconode.ACONode instance at 0x2c713b0>, 'initial': True, 'solution': False}), (16212338162585125650L, {'node': <aconode.ACONode instance at 0x2c71368>}), (17225648078743487250L, {'node': <aconode.ACONode instance at 0x2c71878>}), (17270683387822424850L, {'node': <aconode.ACONode instance at 0x2c718c0>}), (17270672049108763410L, {'node': <aconode.ACONode instance at 0x2c71908>}), (16189824631214261010L, {'node': <aconode.ACONode instance at 0x2c71950>}), (1057729883249394450, {'node': <aconode.ACONode instance at 0x2c71998>}), (14892576832299025170L, {'node': <aconode.ACONode instance at 0x2c719e0>}), (14892717567639896850L, {'node': <aconode.ACONode instance at 0x2c71a28>}), (14892717569401504530L, {'node': <aconode.ACONode instance at 0x2c71a70>}), (14892717569451835410L, {'node': <aconode.ACONode instance at 0x2c71ab8>}), (14892717569451820050L, {'node': <aconode.ACONode instance at 0x2c71b00>}), (14892717567572800530L, {'node': <aconode.ACONode instance at 0x2c71b48>}), (14892717568327775250L, {'node': <aconode.ACONode instance at 0x2c71b90>}), (14892717568422147090L, {'node': <aconode.ACONode instance at 0x2c71bd8>}), (14892716812519437330L, {'node': <aconode.ACONode instance at 0x2c71c20>}), (14847681503440499730L, {'node': <aconode.ACONode instance at 0x2c71c68>}), (13901925581692695570L, {'node': <aconode.ACONode instance at 0x2c71cb0>}), (14982772999587197970L, {'node': <aconode.ACONode instance at 0x2c71cf8>}), (14982779596556301330L, {'node': <aconode.ACONode instance at 0x2c71d40>}), (14982779595801326610L, {'node': <aconode.ACONode instance at 0x2c71d88>}), (14982779597680346130L, {'node': <aconode.ACONode instance at 0x2c71dd0>}), (14982779597680361490L, {'node': <aconode.ACONode instance at 0x2c71e18>}), (14982779597630030610L, {'node': <aconode.ACONode instance at 0x2c71e60>}), (14982779597803045650L, {'node': <aconode.ACONode instance at 0x2c71ea8>}), (14982779597804094210L, {'node': <aconode.ACONode instance at 0x2c71ef0>}), (14982779597804094240L, {'node': <aconode.ACONode instance at 0x2c71f38>}), (14982779597803766565L, {'node': <aconode.ACONode instance at 0x2c71f80>}), (14982779559149650725L, {'node': <aconode.ACONode instance at 0x2c71fc8>}), (14982778914904556325L, {'node': <aconode.ACONode instance at 0x2c72050>}), (14982772730151650085L, {'node': <aconode.ACONode instance at 0x2c72098>}), (14982784824595006245L, {'node': <aconode.ACONode instance at 0x2c720e0>}), (14982784824645337125L, {'node': <aconode.ACONode instance at 0x2c72128>}), (14982784824645321765L, {'node': <aconode.ACONode instance at 0x2c72170>}), (14982784822766302245L, {'node': <aconode.ACONode instance at 0x2c721b8>}), (14982784823521276965L, {'node': <aconode.ACONode instance at 0x2c72200>}), (14982784823588384805L, {'node': <aconode.ACONode instance at 0x2c72248>}), (14982784823588357925L, {'node': <aconode.ACONode instance at 0x2c72290>}), (14982784822783063845L, {'node': <aconode.ACONode instance at 0x2c722d8>}), (14982784823789696805L, {'node': <aconode.ACONode instance at 0x2c72320>}), (14982784823907135525L, {'node': <aconode.ACONode instance at 0x2c72368>}), (14982784823907136005L, {'node': <aconode.ACONode instance at 0x2c723b0>}), (14982784823906087445L, {'node': <aconode.ACONode instance at 0x2c723f8>}), (14982784411595518485L, {'node': <aconode.ACONode instance at 0x2c72440>}), (14982785055840612885L, {'node': <aconode.ACONode instance at 0x2c72488>}), (14982785094494728725L, {'node': <aconode.ACONode instance at 0x2c724d0>}), (14982785094488830485L, {'node': <aconode.ACONode instance at 0x2c72518>}), (14982784407304548885L, {'node': <aconode.ACONode instance at 0x2c72560>}), (14919734974594036245L, {'node': <aconode.ACONode instance at 0x2c725a8>}), (14974622595052614165L, {'node': <aconode.ACONode instance at 0x2c725f0>}), (14977155831188304405L, {'node': <aconode.ACONode instance at 0x2c72638>}), (14977154929245172245L, {'node': <aconode.ACONode instance at 0x2c72680>}), (14977155616429453845L, {'node': <aconode.ACONode instance at 0x2c726c8>}), (14977155616435352085L, {'node': <aconode.ACONode instance at 0x2c72710>}), (14977155616435679760L, {'node': <aconode.ACONode instance at 0x2c72758>}), (14977155616435679745L, {'node': <aconode.ACONode instance at 0x2c727a0>}), (14977155616435679265L, {'node': <aconode.ACONode instance at 0x2c727e8>}), (14977155616435667745L, {'node': <aconode.ACONode instance at 0x2c72830>}), (14977155615361942305L, {'node': <aconode.ACONode instance at 0x2c72878>}), (14977155617123549985L, {'node': <aconode.ACONode instance at 0x2c728c0>}), (14977155617173880865L, {'node': <aconode.ACONode instance at 0x2c72908>}), (14977155617173865505L, {'node': <aconode.ACONode instance at 0x2c72950>}), (14977155615294845985L, {'node': <aconode.ACONode instance at 0x2c72998>}), (14977155616049820705L, {'node': <aconode.ACONode instance at 0x2c729e0>}), (14977155616144192545L, {'node': <aconode.ACONode instance at 0x2c72a28>}), (14977155616146289665L, {'node': <aconode.ACONode instance at 0x2c72a70>}), (14977155616146288705L, {'node': <aconode.ACONode instance at 0x2c72ab8>}), (14977155616146261825L, {'node': <aconode.ACONode instance at 0x2c72b00>}), (14977155615340967745L, {'node': <aconode.ACONode instance at 0x2c72b48>}), (14977155616850917185L, {'node': <aconode.ACONode instance at 0x2c72b90>}), (14977155616968355905L, {'node': <aconode.ACONode instance at 0x2c72bd8>}), (14977155616968344385L, {'node': <aconode.ACONode instance at 0x2c72c20>}), (14977155615357756225L, {'node': <aconode.ACONode instance at 0x2c72c68>}), (14977155617119363905L, {'node': <aconode.ACONode instance at 0x2c72cb0>}), (14977155617169694785L, {'node': <aconode.ACONode instance at 0x2c72cf8>}), (14977155617169671745L, {'node': <aconode.ACONode instance at 0x2c72d40>}), (14977155615290652225L, {'node': <aconode.ACONode instance at 0x2c72dd0>}), (14977155616045626945L, {'node': <aconode.ACONode instance at 0x2c72ea8>}), (14977155616146288705L, {'node': <aconode.ACONode instance at 0x2c72ab8>}), (14977155616146289665L, {'node': <aconode.ACONode instance at 0x2c72a70>}), (14977155616144192545L, {'node': <aconode.ACONode instance at 0x2c72a28>}), (14977155616049820705L, {'node': <aconode.ACONode instance at 0x2c729e0>}), (14977155615294845985L, {'node': <aconode.ACONode instance at 0x2c72998>}), (14977155617173865505L, {'node': <aconode.ACONode instance at 0x2c72950>}), (14977155617173880865L, {'node': <aconode.ACONode instance at 0x2c72908>}), (14977155617123549985L, {'node': <aconode.ACONode instance at 0x2c728c0>}), (14977155615361942305L, {'node': <aconode.ACONode instance at 0x2c72878>}), (14977155616435667745L, {'node': <aconode.ACONode instance at 0x2c72830>}), (14977155616435679265L, {'node': <aconode.ACONode instance at 0x2c727e8>}), (14977155616435679745L, {'node': <aconode.ACONode instance at 0x2c727a0>}), (14977155616429388385L, {'node': <aconode.ACONode instance at 0x2c74368>}), (14977154929245106785L, {'node': <aconode.ACONode instance at 0x2c74488>}), (14977155831188238945L, {'node': <aconode.ACONode instance at 0x2c745a8>}), (14974622595052548705L, {'node': <aconode.ACONode instance at 0x2c746c8>}), (14919734974593970785L, {'node': <aconode.ACONode instance at 0x2c747e8>}), (14982784407304483425L, {'node': <aconode.ACONode instance at 0x2c74908>}), (14982785094488765025L, {'node': <aconode.ACONode instance at 0x2c74a28>}), (14982785094495056385L, {'node': <aconode.ACONode instance at 0x2c74b48>}), (14982785094495055905L, {'node': <aconode.ACONode instance at 0x2c74c68>}), (14982785094495044385L, {'node': <aconode.ACONode instance at 0x2c74d88>}), (14982785093421318945L, {'node': <aconode.ACONode instance at 0x2c74ea8>}), (14982644358080447265L, {'node': <aconode.ACONode instance at 0x2c74fc8>}), (1147797409030816545, {'node': <aconode.ACONode instance at 0x2c77128>}), (1147797409030816545, {'node': <aconode.ACONode instance at 0x2c77128>})]]
#print ("Solution "+ str(solution))
g = nx.Graph()
g.add_nodes_from(solution)
#print("graph generated: "+ str(g.node))
g.add_path([s[0] for s in solution])
self.graph = nx.compose(self.graph, g) # self.graph edges have preference over g
list_graphs_return.append(g)
#print ("Update graph len nodes(self.graph): " + str(len(self.graph.nodes())))
#print ("Update graph len edges(self.graph): " + str(len(self.graph.edges())))
i = 0
for l in list_graphs_return:
#print ("Update graph len nodes ("+str(i) + "): " + str(len(l.nodes())))
#print ("Update graph len edges ("+str(i) + "): " + str(len(l.edges())))
i += 1
return list_graphs_return示例13: show_nca
def show_nca(name1, name2, levels=0):
nca_list=nca(name1, name2)
G=json_graph.load(open("static/local_instance.json"))
H=nx.DiGraph()
for each in nca_list:
anc_path=nx.compose(color_path(each[0],'green'),color_path(each[1],'yellow'))
H=nx.compose(H,anc_path)
for i in range(levels):
H=expand_graph(H)
for each in nca_list:
H.node[each[0][0]]['color']='red' #color the nca different
data=json_graph.dumps(H)
return data示例14: total_overlap
def total_overlap(network1, network2, d="directed"):
"""Returns value of total overlap measure for
two given networks of the same sets of nodes.
Parameters
----------
network1 : first network edge list
network2 : second network edge list
d : directed or undirected
type of graph
Returns
-------
t_overlap : float
"""
if d == "directed":
g1 = nx.read_weighted_edgelist(network1, create_using=nx.DiGraph())
g2 = nx.read_weighted_edgelist(network2, create_using=nx.DiGraph())
elif d == "undirected":
g1 = nx.read_weighted_edgelist(network1)
g2 = nx.read_weighted_edgelist(network2)
overlap = 0
for i in g1.edges():
if g2.has_edge(i[0], i[1]):
overlap += 1
t_overlap = (float(overlap) / float(nx.compose(g1, g2).number_of_edges()))
return t_overlap示例15: jaccard
def jaccard(network1, network2, d="directed"):
"""Returns Jaccard similarity coefficient and
distance of two different networks of the same
sets of nodes.
Parameters
----------
network1 : first network edge list
network2 : second network edge list
d : directed or undirected
type of graph
Returns
-------
j : float
Jaccard similarity coefficient
jd : float
Jaccard distance
"""
if d == "directed":
g1 = nx.read_weighted_edgelist(network1, create_using=nx.DiGraph())
g2 = nx.read_weighted_edgelist(network2, create_using=nx.DiGraph())
elif d == "undirected":
g1 = nx.read_weighted_edgelist(network1)
g2 = nx.read_weighted_edgelist(network2)
union = nx.compose(g1, g2)
inter = nx.intersection(g1, g2)
j = float(inter.number_of_edges()) / float(union.number_of_edges())
jd = 1 - j
return j, jd